This paper presentation investigates the application of multi-satellite temporal analysis and automated surface classification to map spatial evolution patterns of industrial parks in the Global South. As zones of logistical intensity, production, and transnational investment, these industrial landscapes are rapidly expanding yet often under-documented in conventional urban research. Leveraging multi-decade Landsat and Sentinel-2 archives through Google Earth Engine, we employ spectral index classification and temporal change detection algorithms to identify spatial patterns, quantify surface composition changes, and assess industrial isolation characteristics across diverse geographies. Our methodology extends validated frameworks for large-scale infrastructure spatial analysis, particularly multi-spectral surface classification and isolation index development for quantifying urban integration characteristics. The research focuses on selected industrial parks in Southeast Asia and Sub-Saharan Africa, applying a comprehensive temporal approach spanning four decades of satellite observation. Automated classification models distinguish water, vegetation, urban surfaces, and industrial areas using NDVI, NDBI, MNDWI, and EVI indices, validated against Open- StreetMap ground truth data to quantify both formal and informal development trajectories across extended time periods.